Gender: Male 
Age: 35-44
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Single 
First language: English 
Religion: Christian 
Education: High school or below
Mother's education: Graduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 2
Home: Own 
Employment: Self-employed part time
Occupation: Service and sales workers (whose who provide personal and protective services related to travel, housekeeping, catering, personal care, protection against fire and unlawful acts)
Mother's occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Hobbies: Listen to rock/indie music;Listen to hiphop and rap;Go to gigs;Exercise;Use social media;Watch sports 
Devices used daily: Smartphone;Tablet;Other Desktop
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants) 
NLP tools they would find useful: Spell checker (i.e., correcting misspellings);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
Frequency of LLM use: Rarely
LLMs used: Google Gemini 
LLM use cases: Summarizing long texts 
Contexts of LLM use: Learning 
Prompt 1: how much is 30% of $45.00
Prompt 2: How many feet in a meter
Prompt 3: What are some top modern progressive rock bands
Prompt 4: What are some bands similar to The Mars Volta
Prompt 5: Could to explain what defines post-hardcore music
Prompt 6: What are the most influential post-hardcore bands
Prompt 7: how far is vermillion from norwalk
Prompt 8: What is the average miles per fill-up on a toyota corolla
Prompt 9: how many miles to go between oil changes
Prompt 10: is synthetic oil better